Initial COVID OSCTR samples RNAseq Analysis
Miles Smith
18 March, 2021
Numbers of controls and cases
| disease_class | n | percent |
|---|---|---|
| control | 155 | 46.69% |
| infected | 177 | 53.31% |
Differential Gene Expression
| comparison | up | up % | down | down % | mean count cutoff | low counts | low counts % |
|---|---|---|---|---|---|---|---|
| infected vs control | 2058 | 4.66 % | 3348 | 7.58 % | 0 | 14562 | 32.96 % |
| Note: | |||||||
|
44183 with nonzero total read count Outliers: 0 |
25 genes with largest negative log-fold change in experimental group
| gene | log2FoldChange | lfc standard error | pvalue* | padj† |
|---|---|---|---|---|
| AC090227.1 | 12.0 | 3.30 | 0.0e+00 | 1.3e-17 |
| MTCO1P40 | 6.3 | 0.99 | 0.0e+00 | 2.3e-15 |
| AC005258.1 | 4.0 | 0.70 | 2.0e-07 | 5.9e-06 |
| MTND1P23 | 3.4 | 0.68 | 0.0e+00 | 1.2e-08 |
| AL355916.3 | 3.2 | 0.47 | 0.0e+00 | 1.6e-09 |
| AC004057.1 | 2.7 | 0.47 | 0.0e+00 | 1.1e-07 |
| AC006238.2 | 2.7 | 0.71 | 8.9e-03 | 0.049 |
| RPL29P11 | 2.5 | 0.76 | 1.0e-07 | 4.2e-06 |
| CR848007.2 | 2.3 | 0.52 | 1.0e-04 | 0.0014 |
| AC093765.2 | 2.1 | 0.30 | 0.0e+00 | 1.6e-11 |
| LINC02088 | 2.1 | 0.69 | 7.7e-03 | 0.044 |
| MTCO1P12 | 2.1 | 0.69 | 2.8e-06 | 6.3e-05 |
| AC093843.2 | 2.0 | 0.37 | 1.0e-07 | 4.4e-06 |
| KRT8P11 | 2.0 | 0.44 | 6.9e-05 | 0.00098 |
| AC016383.2 | 1.9 | 0.35 | 0.0e+00 | 1.4e-06 |
| AC093772.1 | 1.9 | 0.32 | 0.0e+00 | 7.3e-07 |
| IGLVI-70 | 1.9 | 0.29 | 0.0e+00 | 6.4e-12 |
| LINC02240 | 1.9 | 0.31 | 0.0e+00 | 4.4e-08 |
| ST8SIA5 | 1.9 | 0.28 | 0.0e+00 | 2.2e-11 |
| AC026785.2 | 1.8 | 0.35 | 4.0e-07 | 1e-05 |
| LINC01189 | 1.8 | 0.30 | 0.0e+00 | 3.9e-08 |
| FMO1 | 1.7 | 0.27 | 0.0e+00 | 2.6e-09 |
| HCG4P8 | 1.7 | 0.28 | 0.0e+00 | 4.2e-09 |
| 7SK | 1.5 | 0.23 | 0.0e+00 | 5e-11 |
| GSTA7P | 1.5 | 0.64 | 2.2e-03 | 0.016 |
| IGLV1-44 | 1.5 | 0.11 | 0.0e+00 | 2.7e-38 |
| IGLV9-49 | 1.5 | 0.15 | 0.0e+00 | 8.7e-22 |
| POM121L6P | 1.5 | 0.43 | 8.0e-04 | 0.0074 |
| * Wald test p-values | ||||
| † Benjamini–Hochberg adjusted value |
25 genes with largest positive log-fold change in experimental group
| gene | log2FoldChange | lfc standard error | pvalue* | padj† |
|---|---|---|---|---|
| KDM5D | 2.2 | 0.43 | 0.0e+00 | 5e-08 |
| BCORP1 | 2.0 | 0.63 | 4.2e-06 | 9.1e-05 |
| EIF1AY | 2.0 | 0.81 | 4.0e-05 | 0.00062 |
| AC244213.1 | 1.9 | 0.57 | 1.1e-04 | 0.0015 |
| PRKY | 1.9 | 0.19 | 0.0e+00 | 5.8e-22 |
| TMSB4Y | 1.9 | 0.73 | 8.3e-04 | 0.0076 |
| UTY | 1.9 | 0.25 | 0.0e+00 | 1.1e-14 |
| ZFY | 1.9 | 0.24 | 0.0e+00 | 5.5e-16 |
| KDM5DP1 | 1.8 | 0.58 | 4.8e-04 | 0.0049 |
| USP9Y | 1.7 | 0.17 | 0.0e+00 | 1e-24 |
| AC010889.1 | 1.6 | 0.42 | 5.1e-06 | 0.00011 |
| TLK2P1 | 1.6 | 0.42 | 1.3e-04 | 0.0016 |
| NLGN4Y | 1.5 | 0.40 | 4.0e-07 | 1.3e-05 |
| LINC00278 | 1.4 | 0.11 | 0.0e+00 | 3.9e-35 |
| ZFY-AS1 | 1.4 | 0.61 | 3.2e-03 | 0.022 |
| AC096861.2 | 1.2 | 0.18 | 0.0e+00 | 1.3e-11 |
| CCDC192 | 1.2 | 0.26 | 2.0e-07 | 5.7e-06 |
| CR383656.13 | 1.2 | 0.29 | 2.6e-06 | 6e-05 |
| CTBP2P6 | 1.2 | 0.23 | 0.0e+00 | 1.1e-07 |
| FIGN | 1.2 | 0.44 | 2.6e-04 | 0.0029 |
| RPS27P25 | 1.2 | 0.50 | 5.5e-03 | 0.034 |
| AC008391.1 | 1.1 | 0.29 | 2.3e-05 | 0.00039 |
| AC097110.1 | 1.1 | 0.16 | 0.0e+00 | 2.7e-11 |
| AC109129.1 | 1.1 | 0.24 | 1.0e-07 | 3.3e-06 |
| AL139286.3 | 1.1 | 0.43 | 5.9e-04 | 0.0057 |
| HMGN2P17 | 1.1 | 0.18 | 0.0e+00 | 5.9e-10 |
| RAP1GAP | 1.1 | 0.17 | 0.0e+00 | 6.1e-10 |
| * Wald test p-values | ||||
| † Benjamini–Hochberg adjusted value |
Expression of top class DE genes by class
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
Expression of all top DE genes
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
Volcano plot of gene expression
Expression of selected interferon-stimulated genes
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
Expression of individual genes in the M1.2, M3.4, and M5.12 gene modules
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
Sample Clustering
The eigenvales for the annotated Banchereau modules were used to calculate an adjacentcy matrix, which was in turn used to calculate the gap statistic to determine the optimal k-clusters.
Clustering and variation of samples in reduced dimensional space
PCA
PCA by disease classification
PCA by plate
PCA by ethnicity
PCA by K-means cluster
UMAP
UMAP by disease classification
UMAP by plate
UMAP by ethnicity
UMAP by K-means cluster
Module scores
SLE Interferon module scores
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
SLE Inflammatory module scores
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
Low-density granulocyte module scores
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
SLE Modules with an annotated associated pathway
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
All SLE modules
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
Expression of the annotated Banchereau modules
By disease classification
By cluster
WGCNA
Expression similarity dendrogram
Expression of identified modules
By disease classification
By cluster
WGCNA eigenvalues
Sorted by disease classification
Sorted by cluster
Sorted by hierarchical clustering
Gene ontology GSEA of WGCNA modules
Use of WGCNA modules in classifying clusters.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
Note: Subjects with LP were excluded due to the small population size.
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8 9 10 11 12
## 1 1 1 0 0 0 0 1 0 0 0 0 2
## 2 0 0 0 0 0 0 1 0 0 0 0 0
## 3 0 0 1 0 0 1 0 0 1 0 0 0
## 4 0 0 1 1 1 0 0 3 1 1 2 0
## 5 0 0 1 2 5 0 0 0 1 1 3 0
## 6 1 0 2 1 0 1 2 0 0 1 0 3
## 7 0 0 0 0 0 1 0 0 0 1 1 1
## 8 0 0 1 0 0 1 0 0 0 2 2 0
## 9 0 0 0 1 0 0 0 0 1 0 0 0
## 10 0 0 1 0 0 0 0 0 1 0 0 0
## 11 0 0 0 4 1 1 0 1 0 0 2 0
## 12 3 1 4 0 0 3 3 0 1 0 0 1
##
## Overall Statistics
##
## Accuracy : 0.1585
## 95% CI : (0.0872, 0.2558)
## No Information Rate : 0.1341
## P-Value [Acc > NIR] : 0.3029
##
## Kappa : 0.075
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8 Class: 9 Class: 10 Class: 11 Class: 12
## Sensitivity 0.20000 0.00000 0.09091 0.1111 0.71429 0.12500 0.00000 0.00000 0.16667 0.00000 0.20000 0.14286
## Specificity 0.94805 0.98750 0.97183 0.8767 0.89333 0.86486 0.94667 0.92308 0.98684 0.97368 0.90278 0.80000
## Pos Pred Value 0.20000 0.00000 0.33333 0.1000 0.38462 0.09091 0.00000 0.00000 0.50000 0.00000 0.22222 0.06250
## Neg Pred Value 0.94805 0.97531 0.87342 0.8889 0.97101 0.90141 0.91026 0.94737 0.93750 0.92500 0.89041 0.90909
## Prevalence 0.06098 0.02439 0.13415 0.1098 0.08537 0.09756 0.08537 0.04878 0.07317 0.07317 0.12195 0.08537
## Detection Rate 0.01220 0.00000 0.01220 0.0122 0.06098 0.01220 0.00000 0.00000 0.01220 0.00000 0.02439 0.01220
## Detection Prevalence 0.06098 0.01220 0.03659 0.1220 0.15854 0.13415 0.04878 0.07317 0.02439 0.02439 0.10976 0.19512
## Balanced Accuracy 0.57403 0.49375 0.53137 0.4939 0.80381 0.49493 0.47333 0.46154 0.57675 0.48684 0.55139 0.47143The relative importance of each eigengene in classification:
## parRF variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 30)
##
## 1 2 3 4 5 6 7 8 9 10 11 12
## MEdarkorange -1.3952 1.89887 2.2487 -0.59569 0.1184 1.36791 2.57927 0.69672 0.49439 1.321858 2.7536 7.4600
## MEgreen 6.3469 0.18714 -0.1260 3.48698 3.2494 -0.31395 3.78028 -1.02585 -3.09892 -1.105285 -1.0086 0.3674
## MEcyan 1.5556 4.04098 0.4659 1.69556 4.7611 0.06799 4.71986 0.99135 0.94408 0.810165 2.5551 5.8870
## MEorange 3.4790 2.16043 -1.1680 0.68524 5.7286 0.39011 1.24308 1.27588 -0.43506 0.182313 2.7944 4.0107
## MEpink 5.3879 0.25300 0.2373 1.27497 2.8158 0.36091 -1.04704 -0.41999 -3.01515 -0.337741 -1.2650 1.0739
## MEwhite 2.1193 0.41613 1.9023 -0.69131 -1.0967 1.60930 1.27355 2.24060 -0.04428 -1.393684 5.0311 -0.1569
## MEskyblue 1.3933 0.61022 2.1430 1.06542 4.9282 -0.05518 0.37329 -0.57075 0.37477 -1.104002 3.6126 4.5060
## MEsalmon 4.7574 0.40597 1.0174 1.79920 1.2272 1.62260 2.34827 0.72911 0.53984 0.921454 -0.2533 0.2304
## MEmidnightblue 2.0613 2.90382 -0.6791 3.28034 4.7095 0.83694 2.22896 -1.26325 -1.38901 0.008223 3.7099 2.4345
## MEmagenta 3.4529 2.55145 -2.7483 -1.03871 3.7382 1.02076 4.17258 -0.41356 -1.30924 -1.058841 -0.2308 2.4149
## MEred 1.0351 -0.78341 0.2315 -0.34228 2.6509 4.06632 0.58416 -1.20773 -2.03843 -1.072346 3.4526 -0.2423
## MEsteelblue 0.8806 1.20487 1.6769 0.02673 2.7685 1.91906 -1.04167 0.57742 -0.32796 -1.610384 3.5512 1.5444
## MEyellow -0.6784 1.53055 1.0733 -0.88162 1.3084 -0.55748 -0.04136 0.69660 -0.97973 1.140135 0.6219 3.5321
## MEroyalblue 1.6961 3.39865 -0.7374 -0.71418 0.1547 0.94313 -0.85468 -0.03278 0.21498 1.155358 -0.9200 0.6676
## MEpurple -0.1744 0.07704 -0.2604 -1.47635 0.5152 -0.06305 0.89692 2.38490 -1.84506 0.101767 1.0979 3.2850
## MEdarkturquoise 0.3875 1.09676 -2.2895 0.23815 -0.8247 -0.04158 0.16045 -0.50402 -0.54100 0.595311 3.1920 -1.3016
## MEbrown 1.1030 0.28224 -0.5311 0.84570 2.5224 3.13080 1.38252 0.15978 0.51037 -0.302476 1.9038 2.5596
## MEtan -1.4853 2.52392 1.6129 -0.39620 -1.0376 0.81379 0.60229 -1.39512 -0.62143 0.610480 0.9588 2.9527
## MEdarkgreen 2.2749 -1.09585 0.4798 1.48870 2.9515 1.21173 -0.77545 0.85379 -3.30784 -1.728997 0.9729 1.2572
## MEblack 0.5813 0.87160 0.3770 -0.29125 2.7848 0.46714 1.13752 -1.53809 -0.32256 -0.990952 -0.8510 -0.2955Use of WGCNA modules in labeling disease class.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction control infected
## control 29 1
## infected 9 43
##
## Accuracy : 0.878
## 95% CI : (0.7871, 0.9399)
## No Information Rate : 0.5366
## P-Value [Acc > NIR] : 3.957e-11
##
## Kappa : 0.7512
##
## Mcnemar's Test P-Value : 0.02686
##
## Sensitivity : 0.7632
## Specificity : 0.9773
## Pos Pred Value : 0.9667
## Neg Pred Value : 0.8269
## Prevalence : 0.4634
## Detection Rate : 0.3537
## Detection Prevalence : 0.3659
## Balanced Accuracy : 0.8702
##
## 'Positive' Class : control
## The relative importance of each eigengene in classification:
## parRF variable importance
##
## only 20 most important variables shown (out of 30)
##
## Overall
## MEskyblue 49.000
## MEsteelblue 13.434
## MElightyellow 9.624
## MEwhite 7.969
## MEbrown 3.920
## MEroyalblue 3.618
## MEsaddlebrown 3.490
## MElightgreen 2.640
## MEtan 2.217
## MEred 2.203
## MEcyan 2.025
## MEdarkorange 1.948
## MEpurple 1.946
## MEgrey60 1.900
## MEblack 1.820
## MEmidnightblue 1.812
## MEblue 1.701
## MEdarkred 1.420
## MEmagenta 1.234
## MElightcyan 1.130Use of module modules in classifying clusters.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7 8 9 10 11 12
## 1 1 0 1 0 0 1 0 0 0 0 1 1
## 2 0 1 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 2 0 1 3 0 1 1
## 4 1 0 2 0 0 1 0 1 1 0 0 0
## 5 0 0 0 2 5 0 0 4 0 1 6 0
## 6 0 0 2 1 0 1 1 0 0 1 0 1
## 7 1 1 1 0 0 0 0 0 0 1 0 4
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 2 0 0 0 0 0 1 1 0
## 10 0 0 0 0 0 0 0 0 0 0 0 0
## 11 0 0 1 3 4 0 1 6 1 3 3 0
## 12 0 0 0 1 0 1 1 0 0 1 0 0
##
## Overall Statistics
##
## Accuracy : 0.1325
## 95% CI : (0.0681, 0.2248)
## No Information Rate : 0.1446
## P-Value [Acc > NIR] : 0.6694
##
## Kappa : 0.0395
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6 Class: 7 Class: 8 Class: 9 Class: 10 Class: 11 Class: 12
## Sensitivity 0.33333 0.50000 0.00000 0.00000 0.55556 0.16667 0.00000 0.0000 0.00000 0.00000 0.25000 0.00000
## Specificity 0.95000 1.00000 0.89474 0.91892 0.82432 0.92208 0.90000 1.0000 0.94872 1.00000 0.73239 0.94737
## Pos Pred Value 0.20000 1.00000 0.00000 0.00000 0.27778 0.14286 0.00000 NaN 0.00000 NaN 0.13636 0.00000
## Neg Pred Value 0.97436 0.98780 0.90667 0.88312 0.93846 0.93421 0.96000 0.8554 0.93671 0.90361 0.85246 0.91139
## Prevalence 0.03614 0.02410 0.08434 0.10843 0.10843 0.07229 0.03614 0.1446 0.06024 0.09639 0.14458 0.08434
## Detection Rate 0.01205 0.01205 0.00000 0.00000 0.06024 0.01205 0.00000 0.0000 0.00000 0.00000 0.03614 0.00000
## Detection Prevalence 0.06024 0.01205 0.09639 0.07229 0.21687 0.08434 0.09639 0.0000 0.04819 0.00000 0.26506 0.04819
## Balanced Accuracy 0.64167 0.75000 0.44737 0.45946 0.68994 0.54437 0.45000 0.5000 0.47436 0.50000 0.49120 0.47368The relative importance of each eigengene in classification:
## parRF variable importance
##
## variables are sorted by maximum importance across the classes
## only 20 most important variables shown (out of 260)
##
## 1 2 3 4 5 6 7 8 9 10 11 12
## M8.73 6.6793 1.8735 -0.24795 3.1531 3.4482 -0.7082 1.93138 -0.2403 -1.5476 -0.9622 -1.749856 -0.81022
## M9.3 -1.2981 -1.0112 2.35450 3.0177 5.2362 1.2023 2.11434 -1.6746 -0.1239 -0.1487 2.990503 1.18935
## M1.2 1.3435 1.6691 -0.53550 1.9847 4.7295 -0.1790 1.97096 -0.5557 1.4660 -1.0010 0.959979 1.04364
## M8.9 4.0834 0.7813 2.59897 4.6416 3.3387 -1.6693 1.63225 -1.9281 -1.9441 0.7893 3.262826 -0.06088
## M9.46 2.1446 1.3894 -1.50550 2.2101 3.8952 0.6430 1.66489 -1.6358 -1.4446 0.1105 0.002465 0.35608
## M8.3 1.3214 1.2669 1.97169 1.6291 2.9199 0.7761 0.89539 0.3209 -2.4928 -2.4663 3.860012 1.68357
## M8.53 -2.7434 1.2171 0.01224 1.2811 2.8302 -2.2821 3.64189 0.8152 -1.6527 -0.3957 3.312052 2.81689
## M7.35 1.8782 0.0000 -0.74860 0.1429 0.5935 0.7424 0.01569 -0.2315 -0.1086 -0.1280 3.626360 0.76324
## M7.32 2.9070 0.0000 0.05742 1.3509 0.3823 3.5991 1.07274 -0.6475 0.4066 1.1046 0.358513 -0.30210
## M4.8 1.7370 3.4818 1.55086 1.9600 0.4727 0.7437 -0.64545 -1.0010 -0.2665 0.5185 0.584613 -0.44510
## M9.37 0.8873 1.5110 -0.67573 1.5960 3.4731 -0.7560 0.40651 -1.7371 -1.5640 1.0319 0.232192 -2.36505
## M8.42 0.9748 1.4171 3.12432 2.2979 3.4479 1.2490 0.97418 -1.5447 -1.1969 -0.2515 1.231625 0.10545
## M5.9 3.4165 1.0010 0.23923 2.0566 1.0024 1.0742 2.32143 0.9070 -0.4159 -0.6738 0.369391 -0.03494
## M8.28 2.5649 0.1280 3.14703 0.9509 2.2879 1.7754 0.09200 -0.1176 -2.2778 0.5752 3.218694 0.23017
## M8.105 3.1713 0.0000 1.32364 0.7301 2.0592 0.8571 0.27107 -0.1174 -0.5687 -0.8710 -0.324682 -1.96532
## M8.82 1.4083 -1.0010 1.00100 0.3491 3.1305 0.5594 2.06440 -0.8122 -1.0996 1.0010 -1.473120 1.00100
## M7.13 -2.5093 -0.4473 1.89518 -0.1068 3.1141 1.1832 0.44730 -0.8461 0.5645 1.0010 0.564208 0.06762
## M8.95 1.5551 -1.0010 1.01522 1.0513 3.0750 0.8043 -0.35305 -1.5749 -0.3166 -1.1729 -0.150149 0.32883
## M9.25 -1.2565 0.4890 2.63519 -0.0558 0.9286 0.4289 -0.28561 -1.9856 1.1310 -1.3178 3.068939 0.58601
## M8.6 0.9923 0.3847 0.31963 0.3966 0.8528 1.2223 -0.60246 -0.4696 -1.0438 2.9758 2.584557 -1.01800Use of module modules in labeling disease class.
The module eigengene scores were used to train a random forest model with repeated cross validation to predict cluster identity.
The model was trained with 75% of the dataset and tested on 25%.
## Confusion Matrix and Statistics
##
## Reference
## Prediction control infected
## control 26 4
## infected 12 40
##
## Accuracy : 0.8049
## 95% CI : (0.7026, 0.8842)
## No Information Rate : 0.5366
## P-Value [Acc > NIR] : 3.701e-07
##
## Kappa : 0.6019
##
## Mcnemar's Test P-Value : 0.08012
##
## Sensitivity : 0.6842
## Specificity : 0.9091
## Pos Pred Value : 0.8667
## Neg Pred Value : 0.7692
## Prevalence : 0.4634
## Detection Rate : 0.3171
## Detection Prevalence : 0.3659
## Balanced Accuracy : 0.7967
##
## 'Positive' Class : control
## The relative importance of each eigengene in classification:
## parRF variable importance
##
## only 20 most important variables shown (out of 260)
##
## Importance
## M5.15 25.344
## M1.2 18.188
## M7.26 8.437
## M4.11 7.449
## M8.56 7.384
## M3.4 4.417
## M9.32 4.231
## M9.41 4.076
## M9.11 3.728
## M8.15 3.582
## M1.1 3.470
## M8.53 3.418
## M8.16 3.254
## M8.59 2.933
## M8.30 2.684
## M7.13 2.543
## M8.63 2.416
## M8.95 2.292
## M9.52 2.169
## M2.3 2.115## ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
## setting value
## version R version 4.0.4 (2021-02-15)
## os Ubuntu 20.04 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate en_US.UTF-8
## ctype en_US.UTF-8
## tz Etc/UTC
## date 2021-03-18
##
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## package * version date lib source
## abind 1.4-5 2016-07-21 [1] RSPM (R 4.0.4)
## annotate 1.68.0 2020-10-27 [1] Bioconductor
## AnnotationDbi 1.52.0 2020-10-27 [1] RSPM (R 4.0.4)
## assertthat 0.2.1 2019-03-21 [1] RSPM (R 4.0.3)
## backports 1.2.1 2020-12-09 [1] RSPM (R 4.0.4)
## base64enc 0.1-3 2015-07-28 [1] RSPM (R 4.0.3)
## base64url 1.4 2018-05-14 [1] RSPM (R 4.0.4)
## Biobase * 2.50.0 2020-10-27 [1] RSPM (R 4.0.4)
## BiocGenerics * 0.36.0 2020-10-27 [1] RSPM (R 4.0.4)
## BiocManager 1.30.10 2019-11-16 [1] CRAN (R 4.0.4)
## BiocParallel * 1.24.1 2020-11-06 [1] RSPM (R 4.0.4)
## bit 4.0.4 2020-08-04 [1] RSPM (R 4.0.3)
## bit64 4.0.5 2020-08-30 [1] RSPM (R 4.0.3)
## bitops 1.0-6 2013-08-17 [1] RSPM (R 4.0.4)
## blob 1.2.1 2020-01-20 [1] RSPM (R 4.0.4)
## broom * 0.7.5 2021-02-19 [1] RSPM (R 4.0.4)
## bslib 0.2.4 2021-01-25 [1] RSPM (R 4.0.4)
## cachem 1.0.4 2021-02-13 [1] RSPM (R 4.0.3)
## car 3.0-10 2020-09-29 [1] RSPM (R 4.0.4)
## carData 3.0-4 2020-05-22 [1] RSPM (R 4.0.4)
## caret * 6.0-86 2020-03-20 [1] RSPM (R 4.0.3)
## cellranger 1.1.0 2016-07-27 [1] RSPM (R 4.0.4)
## checkmate 2.0.0 2020-02-06 [1] RSPM (R 4.0.4)
## class 7.3-18 2021-01-24 [2] CRAN (R 4.0.4)
## cli 2.3.1 2021-02-23 [1] RSPM (R 4.0.3)
## cluster * 2.1.1 2021-02-14 [1] RSPM (R 4.0.3)
## clusterProfiler * 3.18.1 2021-02-09 [1] Bioconductor
## codetools 0.2-18 2020-11-04 [2] CRAN (R 4.0.4)
## colorspace 2.0-0 2020-11-11 [1] RSPM (R 4.0.4)
## corrplot * 0.84 2017-10-16 [1] RSPM (R 4.0.4)
## cowplot * 1.1.1 2020-12-30 [1] RSPM (R 4.0.4)
## crayon 1.4.1 2021-02-08 [1] RSPM (R 4.0.3)
## crosstalk 1.1.1 2021-01-12 [1] RSPM (R 4.0.3)
## curl 4.3 2019-12-02 [1] RSPM (R 4.0.3)
## data.table * 1.14.0 2021-02-21 [1] RSPM (R 4.0.4)
## DBI 1.1.1 2021-01-15 [1] RSPM (R 4.0.4)
## dbplyr 2.1.0 2021-02-03 [1] RSPM (R 4.0.4)
## DelayedArray 0.16.2 2021-02-26 [1] RSPM (R 4.0.4)
## DESeq2 * 1.30.1 2021-02-19 [1] RSPM (R 4.0.4)
## dials * 0.0.9 2020-09-16 [1] RSPM (R 4.0.3)
## DiceDesign 1.9 2021-02-13 [1] RSPM (R 4.0.3)
## digest 0.6.27 2020-10-24 [1] RSPM (R 4.0.3)
## DO.db 2.9 2021-03-13 [1] RSPM (R 4.0.4)
## doParallel 1.0.16 2020-10-16 [1] RSPM (R 4.0.4)
## DOSE 3.16.0 2020-10-27 [1] RSPM (R 4.0.4)
## downloader 0.4 2015-07-09 [1] RSPM (R 4.0.4)
## dplyr * 1.0.5 2021-03-05 [1] RSPM (R 4.0.4)
## drake * 7.13.1 2021-02-03 [1] RSPM (R 4.0.4)
## DT 0.17 2021-01-06 [1] RSPM (R 4.0.3)
## dynamicTreeCut * 1.63-1 2016-03-11 [1] RSPM (R 4.0.4)
## e1071 1.7-5 2021-03-15 [1] RSPM (R 4.0.4)
## edgeR 3.32.1 2021-01-14 [1] RSPM (R 4.0.4)
## ellipsis 0.3.1 2020-05-15 [1] RSPM (R 4.0.3)
## enrichplot 1.10.2 2021-01-28 [1] RSPM (R 4.0.4)
## evaluate 0.14 2019-05-28 [1] RSPM (R 4.0.3)
## factoextra * 1.0.7 2020-04-01 [1] RSPM (R 4.0.3)
## fansi 0.4.2 2021-01-15 [1] RSPM (R 4.0.3)
## farver 2.1.0 2021-02-28 [1] RSPM (R 4.0.4)
## fastcluster * 1.1.25 2018-06-07 [1] RSPM (R 4.0.4)
## fastmap 1.1.0 2021-01-25 [1] RSPM (R 4.0.3)
## fastmatch 1.1-0 2017-01-28 [1] RSPM (R 4.0.4)
## fgsea 1.16.0 2020-10-27 [1] RSPM (R 4.0.4)
## filelock 1.0.2 2018-10-05 [1] RSPM (R 4.0.4)
## flextable * 0.6.4 2021-03-10 [1] RSPM (R 4.0.3)
## FNN 1.1.3 2019-02-15 [1] RSPM (R 4.0.4)
## forcats * 0.5.1 2021-01-27 [1] RSPM (R 4.0.4)
## foreach 1.5.1 2020-10-15 [1] RSPM (R 4.0.4)
## foreign 0.8-81 2020-12-22 [2] CRAN (R 4.0.4)
## formattable * 0.2.1 2021-01-07 [1] RSPM (R 4.0.4)
## Formula 1.2-4 2020-10-16 [1] RSPM (R 4.0.4)
## fs 1.5.0 2020-07-31 [1] RSPM (R 4.0.3)
## furrr * 0.2.2 2021-01-29 [1] RSPM (R 4.0.4)
## future * 1.21.0 2020-12-10 [1] RSPM (R 4.0.4)
## gdtools 0.2.3 2021-01-06 [1] RSPM (R 4.0.3)
## genefilter * 1.72.1 2021-01-21 [1] RSPM (R 4.0.4)
## geneplotter 1.68.0 2020-10-27 [1] RSPM (R 4.0.4)
## generics 0.1.0 2020-10-31 [1] RSPM (R 4.0.4)
## GenomeInfoDb * 1.26.4 2021-03-10 [1] Bioconductor
## GenomeInfoDbData 1.2.4 2021-03-13 [1] RSPM (R 4.0.4)
## GenomicRanges * 1.42.0 2020-10-27 [1] RSPM (R 4.0.4)
## ggfittext 0.9.1 2021-01-30 [1] RSPM (R 4.0.4)
## ggforce * 0.3.2.9000 2021-03-17 [1] Github (thomasp85/ggforce@1f17eb3)
## ggplot2 * 3.3.3 2020-12-30 [1] RSPM (R 4.0.4)
## ggplotify * 0.0.5 2020-03-12 [1] RSPM (R 4.0.0)
## ggpubr * 0.4.0 2020-06-27 [1] RSPM (R 4.0.4)
## ggradar * 0.2 2021-03-16 [1] Github (ricardo-bion/ggradar@63e5cef)
## ggraph 2.0.5 2021-02-23 [1] RSPM (R 4.0.4)
## ggrepel * 0.9.1 2021-01-15 [1] RSPM (R 4.0.3)
## ggsignif 0.6.1 2021-02-23 [1] RSPM (R 4.0.4)
## ggtext * 0.1.1 2020-12-17 [1] RSPM (R 4.0.3)
## globals 0.14.0 2020-11-22 [1] RSPM (R 4.0.4)
## glue 1.4.2 2020-08-27 [1] RSPM (R 4.0.3)
## GO.db 3.12.1 2021-03-13 [1] RSPM (R 4.0.4)
## GOSemSim 2.16.1 2020-10-29 [1] RSPM (R 4.0.4)
## gower 0.2.2 2020-06-23 [1] RSPM (R 4.0.3)
## GPfit 1.0-8 2019-02-08 [1] RSPM (R 4.0.3)
## graphlayouts 0.7.1 2020-10-26 [1] RSPM (R 4.0.4)
## gridBase 0.4-7 2014-02-24 [1] RSPM (R 4.0.4)
## gridExtra 2.3 2017-09-09 [1] RSPM (R 4.0.4)
## gridGraphics 0.5-1 2020-12-13 [1] RSPM (R 4.0.3)
## gridtext 0.1.4 2020-12-10 [1] RSPM (R 4.0.4)
## gtable 0.3.0 2019-03-25 [1] RSPM (R 4.0.4)
## gtools * 3.8.2 2020-03-31 [1] RSPM (R 4.0.4)
## haven 2.3.1 2020-06-01 [1] RSPM (R 4.0.4)
## here * 1.0.1 2020-12-13 [1] RSPM (R 4.0.4)
## HGNChelper * 0.8.1 2019-10-24 [1] RSPM (R 4.0.0)
## highr 0.8 2019-03-20 [1] RSPM (R 4.0.3)
## Hmisc 4.5-0 2021-02-28 [1] RSPM (R 4.0.4)
## hms 1.0.0 2021-01-13 [1] RSPM (R 4.0.4)
## htmlTable 2.1.0 2020-09-16 [1] RSPM (R 4.0.4)
## htmltools 0.5.1.1 2021-01-22 [1] RSPM (R 4.0.3)
## htmlwidgets 1.5.3 2020-12-10 [1] RSPM (R 4.0.3)
## httr 1.4.2 2020-07-20 [1] RSPM (R 4.0.3)
## igraph 1.2.6 2020-10-06 [1] RSPM (R 4.0.4)
## import 1.2.0 2020-09-24 [1] RSPM (R 4.0.2)
## impute 1.64.0 2020-10-27 [1] RSPM (R 4.0.4)
## infer * 0.5.4 2021-01-13 [1] RSPM (R 4.0.3)
## inspectdf * 0.0.10 2021-02-20 [1] RSPM (R 4.0.4)
## ipred 0.9-11 2021-03-12 [1] RSPM (R 4.0.3)
## IRanges * 2.24.1 2020-12-12 [1] RSPM (R 4.0.4)
## irlba * 2.3.3 2019-02-05 [1] RSPM (R 4.0.3)
## iterators 1.0.13 2020-10-15 [1] RSPM (R 4.0.4)
## janitor * 2.1.0 2021-01-05 [1] RSPM (R 4.0.4)
## jpeg 0.1-8.1 2019-10-24 [1] RSPM (R 4.0.4)
## jquerylib 0.1.3 2020-12-17 [1] RSPM (R 4.0.4)
## jsonlite 1.7.2 2020-12-09 [1] RSPM (R 4.0.3)
## kableExtra * 1.3.4 2021-02-20 [1] RSPM (R 4.0.4)
## knitr * 1.31 2021-01-27 [1] RSPM (R 4.0.3)
## labeling 0.4.2 2020-10-20 [1] RSPM (R 4.0.4)
## lattice * 0.20-41 2020-04-02 [2] CRAN (R 4.0.4)
## latticeExtra 0.6-29 2019-12-19 [1] RSPM (R 4.0.4)
## lava 1.6.9 2021-03-11 [1] RSPM (R 4.0.3)
## lazyeval 0.2.2 2019-03-15 [1] RSPM (R 4.0.3)
## lhs 1.1.1 2020-10-05 [1] RSPM (R 4.0.3)
## lifecycle 1.0.0 2021-02-15 [1] RSPM (R 4.0.3)
## limma 3.46.0 2020-10-27 [1] RSPM (R 4.0.4)
## listenv 0.8.0 2019-12-05 [1] RSPM (R 4.0.4)
## locfit 1.5-9.4 2020-03-25 [1] RSPM (R 4.0.4)
## lubridate 1.7.10 2021-02-26 [1] RSPM (R 4.0.4)
## magrittr * 2.0.1 2020-11-17 [1] RSPM (R 4.0.3)
## markdown 1.1 2019-08-07 [1] RSPM (R 4.0.3)
## MASS 7.3-53.1 2021-02-12 [2] RSPM (R 4.0.3)
## Matrix * 1.3-2 2021-01-06 [2] CRAN (R 4.0.4)
## MatrixGenerics * 1.2.1 2021-01-30 [1] RSPM (R 4.0.4)
## matrixStats * 0.58.0 2021-01-29 [1] RSPM (R 4.0.4)
## memoise 2.0.0 2021-01-26 [1] RSPM (R 4.0.3)
## mgcv * 1.8-34 2021-02-16 [2] RSPM (R 4.0.3)
## modeldata * 0.1.0 2020-10-22 [1] RSPM (R 4.0.3)
## ModelMetrics 1.2.2.2 2020-03-17 [1] RSPM (R 4.0.3)
## modelr 0.1.8 2020-05-19 [1] RSPM (R 4.0.4)
## moduleScoreR * 0.0.0.9200 2021-03-13 [1] Github (milescsmith/moduleScoreR@e6db70f)
## munsell 0.5.0 2018-06-12 [1] RSPM (R 4.0.4)
## nlme * 3.1-152 2021-02-04 [2] CRAN (R 4.0.4)
## NMF 0.23.0 2020-08-01 [1] RSPM (R 4.0.4)
## nnet 7.3-15 2021-01-24 [2] CRAN (R 4.0.4)
## oaColors * 0.0.4 2015-11-30 [1] RSPM (R 4.0.0)
## officer 0.3.17 2021-03-05 [1] RSPM (R 4.0.3)
## openxlsx 4.2.3 2020-10-27 [1] RSPM (R 4.0.4)
## paletteer * 1.3.0 2021-01-06 [1] RSPM (R 4.0.4)
## parallelDist 0.2.4 2018-12-12 [1] RSPM (R 4.0.4)
## parallelly 1.24.0 2021-03-14 [1] RSPM (R 4.0.3)
## parsnip * 0.1.5 2021-01-19 [1] RSPM (R 4.0.3)
## pheatmap * 1.0.12 2019-01-04 [1] RSPM (R 4.0.4)
## pillar 1.5.1 2021-03-05 [1] RSPM (R 4.0.3)
## pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.0.3)
## pkgmaker 0.32.2 2020-10-20 [1] RSPM (R 4.0.4)
## plotly * 4.9.3 2021-01-10 [1] RSPM (R 4.0.4)
## plyr 1.8.6 2020-03-03 [1] RSPM (R 4.0.4)
## png 0.1-7 2013-12-03 [1] RSPM (R 4.0.4)
## polyclip 1.10-0 2019-03-14 [1] RSPM (R 4.0.4)
## preprocessCore 1.52.1 2021-01-08 [1] RSPM (R 4.0.4)
## prettyunits 1.1.1 2020-01-24 [1] RSPM (R 4.0.3)
## prismatic 1.0.0 2021-01-05 [1] RSPM (R 4.0.4)
## pROC 1.17.0.1 2021-01-13 [1] RSPM (R 4.0.3)
## prodlim 2019.11.13 2019-11-17 [1] RSPM (R 4.0.3)
## progress 1.2.2 2019-05-16 [1] RSPM (R 4.0.4)
## proxy 0.4-25 2021-03-05 [1] RSPM (R 4.0.3)
## purrr * 0.3.4 2020-04-17 [1] RSPM (R 4.0.3)
## qvalue 2.22.0 2020-10-27 [1] RSPM (R 4.0.4)
## R6 2.5.0 2020-10-28 [1] RSPM (R 4.0.3)
## randomForest * 4.6-14 2018-03-25 [1] RSPM (R 4.0.3)
## RColorBrewer * 1.1-2 2014-12-07 [1] RSPM (R 4.0.3)
## Rcpp 1.0.6 2021-01-15 [1] RSPM (R 4.0.4)
## RcppParallel 5.0.3 2021-02-24 [1] RSPM (R 4.0.4)
## RCurl 1.98-1.2 2020-04-18 [1] RSPM (R 4.0.4)
## readr * 1.4.0 2020-10-05 [1] RSPM (R 4.0.4)
## readxl * 1.3.1 2019-03-13 [1] RSPM (R 4.0.3)
## recipes * 0.1.15 2020-11-11 [1] RSPM (R 4.0.3)
## registry 0.5-1 2019-03-05 [1] RSPM (R 4.0.4)
## rematch2 2.1.2 2020-05-01 [1] RSPM (R 4.0.3)
## reprex 1.0.0 2021-01-27 [1] RSPM (R 4.0.4)
## reshape2 1.4.4 2020-04-09 [1] RSPM (R 4.0.4)
## reticulate 1.18 2020-10-25 [1] RSPM (R 4.0.4)
## rio 0.5.26 2021-03-01 [1] RSPM (R 4.0.4)
## rlang * 0.4.10 2020-12-30 [1] RSPM (R 4.0.3)
## rmarkdown * 2.7 2021-02-19 [1] RSPM (R 4.0.4)
## rngtools 1.5 2020-01-23 [1] RSPM (R 4.0.4)
## rpart 4.1-15 2019-04-12 [2] CRAN (R 4.0.4)
## rprojroot 2.0.2 2020-11-15 [1] RSPM (R 4.0.3)
## rsample * 0.0.9 2021-02-17 [1] RSPM (R 4.0.3)
## RSpectra 0.16-0 2019-12-01 [1] RSPM (R 4.0.4)
## RSQLite 2.2.4 2021-03-12 [1] RSPM (R 4.0.3)
## rstatix * 0.7.0 2021-02-13 [1] RSPM (R 4.0.4)
## rstudioapi 0.13 2020-11-12 [1] RSPM (R 4.0.3)
## rsvd 1.0.3 2020-02-17 [1] RSPM (R 4.0.4)
## rvcheck 0.1.8 2020-03-01 [1] RSPM (R 4.0.4)
## rvest 1.0.0 2021-03-09 [1] RSPM (R 4.0.4)
## S4Vectors * 0.28.1 2020-12-09 [1] RSPM (R 4.0.4)
## sass 0.3.1 2021-01-24 [1] RSPM (R 4.0.4)
## scales * 1.1.1 2020-05-11 [1] RSPM (R 4.0.3)
## scatterpie 0.1.5 2020-09-09 [1] RSPM (R 4.0.4)
## sessioninfo 1.1.1 2018-11-05 [1] RSPM (R 4.0.3)
## shadowtext 0.0.7 2019-11-06 [1] RSPM (R 4.0.4)
## snakecase 0.11.0 2019-05-25 [1] RSPM (R 4.0.4)
## snow 0.4-3 2018-09-14 [1] RSPM (R 4.0.4)
## storr 1.2.5 2020-12-01 [1] RSPM (R 4.0.4)
## stringi 1.5.3 2020-09-09 [1] RSPM (R 4.0.3)
## stringr * 1.4.0 2019-02-10 [1] RSPM (R 4.0.3)
## SummarizedExperiment * 1.20.0 2020-10-27 [1] RSPM (R 4.0.4)
## survival 3.2-7 2020-09-28 [2] CRAN (R 4.0.4)
## sva * 3.38.0 2020-10-27 [1] Bioconductor
## svglite 2.0.0 2021-02-20 [1] RSPM (R 4.0.4)
## systemfonts 1.0.1 2021-02-09 [1] RSPM (R 4.0.4)
## tibble * 3.1.0 2021-02-25 [1] RSPM (R 4.0.3)
## tidygraph 1.2.0 2020-05-12 [1] RSPM (R 4.0.4)
## tidymodels * 0.1.2 2020-11-22 [1] RSPM (R 4.0.3)
## tidyr * 1.1.3 2021-03-03 [1] RSPM (R 4.0.4)
## tidyselect 1.1.0 2020-05-11 [1] RSPM (R 4.0.4)
## tidyverse * 1.3.0 2019-11-21 [1] RSPM (R 4.0.4)
## timeDate 3043.102 2018-02-21 [1] RSPM (R 4.0.3)
## tune * 0.1.3 2021-02-28 [1] RSPM (R 4.0.3)
## tweenr 1.0.1 2018-12-14 [1] RSPM (R 4.0.4)
## tximport * 1.18.0 2020-10-27 [1] RSPM (R 4.0.4)
## txtq 0.2.3 2020-06-23 [1] RSPM (R 4.0.4)
## utf8 1.2.1 2021-03-12 [1] RSPM (R 4.0.3)
## uuid 0.1-4 2020-02-26 [1] RSPM (R 4.0.3)
## uwot * 0.1.10 2020-12-15 [1] RSPM (R 4.0.4)
## vctrs 0.3.6 2020-12-17 [1] RSPM (R 4.0.3)
## viridis * 0.5.1 2018-03-29 [1] RSPM (R 4.0.3)
## viridisLite * 0.3.0 2018-02-01 [1] RSPM (R 4.0.4)
## webshot 0.5.2 2019-11-22 [1] RSPM (R 4.0.4)
## WGCNA * 1.70-3 2021-02-28 [1] RSPM (R 4.0.4)
## withr 2.4.1 2021-01-26 [1] RSPM (R 4.0.3)
## workflows * 0.2.2 2021-03-10 [1] RSPM (R 4.0.3)
## xfun 0.22 2021-03-11 [1] RSPM (R 4.0.3)
## XML 3.99-0.5 2020-07-23 [1] RSPM (R 4.0.4)
## xml2 1.3.2 2020-04-23 [1] RSPM (R 4.0.3)
## xtable 1.8-4 2019-04-21 [1] RSPM (R 4.0.4)
## XVector 0.30.0 2020-10-27 [1] RSPM (R 4.0.4)
## yaml 2.2.1 2020-02-01 [1] RSPM (R 4.0.3)
## yardstick * 0.0.7 2020-07-13 [1] RSPM (R 4.0.3)
## zip 2.1.1 2020-08-27 [1] RSPM (R 4.0.3)
## zlibbioc 1.36.0 2020-10-27 [1] RSPM (R 4.0.4)
##
## [1] /usr/local/lib/R/site-library
## [2] /usr/local/lib/R/library